import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
import time
import random

num = random.randint(0, 9)
print(num)
diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)

print(diabetes_X[:, np.newaxis])
print(diabetes_X[:, np.newaxis].shape[2])
print(diabetes_X[:, np.newaxis,3])
diabetes_X = diabetes_X[:, np.newaxis, num]

# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]

diabetes_X_test = diabetes_X[-20:]

# Split the targets into training/testing sets
diabetes_y_train = diabetes_y[:-20]
diabetes_y_test = diabetes_y[-20:]

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)

# Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)
timestamp = str(time.time())
# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print('Mean squared error: %.2f'
      % mean_squared_error(diabetes_y_test, diabetes_y_pred))
# The coefficient of determination: 1 is perfect prediction
print('Coefficient of determination: %.2f'
      % r2_score(diabetes_y_test, diabetes_y_pred))

# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)
plt.savefig(os.path.join(timestamp + ".png"))

plt.xticks(())
plt.yticks(())
plt.clf()